Precise calibration of a binocular vision system is the foundation of binocular vision measurement. In this paper, we propose a highly precise and robust binocular camera calibration method, which is devoted to minimize the error between the geometric relation of 3D reconstructed feature points and the ground truth, such as adjacent distance error, collinear error, and right-angle error. In addition, the reprojection error and epipolar are introduced to satisfy the homography relation and epipolar geometry theory better. We optimize all intrinsic parameters, extrinsic parameters, and distortion parameters to minimize the objective function, which is the sum of a series of nonlinear least squares terms. Levenberg-Marquardt iterative algorithm is used to find the optimal solution of the camera parameters. To test the precision and robustness of the proposed method, both actual measurement experiments and Gauss noise-adding experiments are carried out. The experimental results show that compared with the other two calibration methods in the contrast experiment, the distance measurement error, collinear error, and right-angle error are reduced dramatically. It is noticeable that in Gauss noise-adding experiments, the calibration parameters estimated by the proposed method are more stable.
A novel method of estimating circle pose and radius is proposed, based on binocular vision. This method uses the special vectors that are irrelevant to the unknown radius to calculate the normal vector of the circular plane, which is the foundation for calculating the vanishing line of the circular plane in the imaging plane. The projections of the circle center on the left and right image are calculated afterwards through the vanishing line. Then, the circle center is reconstructed by the triangulation technology of binocular stereo vision. Finally, the radius of the circular target is calculated through the relative positions of points in the same coordinate system. The estimates of normal vector, center position, and radius size are unique and definite, so there is no need for a subsequent screening process. When the radius is unknown, most of the conventional circle pose estimation methods are unusable. However, the proposed method has no prerequisites about the circular target in order to estimate the pose and radius of the circular target, which improves flexibility and extends the application field. Besides estimating the pose of a circular target of unknown radius, the proposed method can also be applied to the vision measurement of the radius of a circular object. Both actual measurement experiments and noise simulation experiments are performed. The results show that the proposed method is more precise and robust than conventional methods.
To improve the multiple performance indices of practical engineering structures under uncertainties, an interval constrained multiobjective optimization model was constructed with structural performance indices included in objectives and constraints being functions of the interval uncertain parameters. An algorithm integrating radial basis function (RBF), interval analysis, and non-dominated sorting genetic algorithm (NSGA-II) was put forward to locate the Pareto-optimal solutions to the interval multiobjective optimization model. A series of RBFs were constructed based on the Latin hypercube experimental design (LHED) and finite element analysis (FEA), which were then integrated with interval analysis to compute the interval bounds of the objective and constraint functions under the fluctuation of uncertain parameters. Then the fitness of every individual during the NSGA-II-based optimization could be obtained. The case study on the optimization of the mechanical performance of a press slider with uncertain material properties demonstrated the feasibility and validity of the proposed methodology.
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